Motivation-Based proposed approach. Moreover, the objective of this

Motivation-Based Online P2P Lending Approach for Loan Project Recommendation System Uppada Gautami Computer science engineering Email [email protected] Abstract Online social lending has become an essential method for the financial support of individual borrowers that meets the goals of individual lenders. So as to find the appropriate lenders to invest the fund and to ease the borrowers difficulties, a technique namely project recommendation is used to solve the addressing issue of borrowers motivation before the lender invests the fund. Because of limitation in exploring the structured information, the current recommendation technique cannot obtain the expected performance to solve the lender-borrower problem.

To solve the above rising issue, this work intuitively mines the voluminous amount of unstructured data that solves the conflict of lender-borrower. Using text mining and classifier technique, the Motivation-based Online P2P Lending approach is proposed which incorporates the collaborative techniques to determine the loan funding progress. In this approach, the feature like loan-temporality is associated with the lender via sliding window in order to examine the temporal loans of borrowers.

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A dataset from popular websites like Kiva and i2ifunding can be used to implement the proposed approach. Moreover, the objective of this approach is to improve inactive lender groups, unpopular loan groups, addresses the data sparsity and also cold start problems in loan project recommendations. Using proposed approach, the voluminous of unstructured text data from several peer-to-peer lending platforms can be effectively examined to solve the information overloading problem in order to achieve better lender-borrower relationship. Keywords Social Lending, Project Recommendations, P2p Lending, Text Mining INTRODUCTION In the past, online social lending has experienced a significant growth.

It becomes one of the essential financing methods between the individual borrowers and the individual lenders. Lenders decide whether the loan projects are so interested to invest their money in a loan request or not. P2P lending model has attract great attentions from both industrial and academic fields.

In the financial industry, P2P model provides a new pattern on group or crowd financial product design and management. For example, Perlman 1 proposes an innovative group financial management system in his pattern 2 do a comparative study on P2P lending products between the USA and China. In the academic field, user behavior pattern and credit or trust model are inspect in the P2P lending scenario 3-5.For example, Lee et al. 6 study the herding behavior in the P2P lending market where seemingly conflicting conditions and features of herding are present. They find strong evidence of herding and its diminishing marginal effect as bidding advances 7 to find the online friendships of borrowers act as signals of credit quality. Friendships increase the probability of successful funding, lower interest rates on funded loans, and are associated with lower ex post default rates 8 investigate the role appearance plays in financial transactions. They find that borrowers who appear more trustworthy have higher probabilities of having their loans funded.

Small and micro enterprises and personal business firms face the problem of a shortage of funds. With the development of e-commercea particular type of lending (online lending)provides a new solution. Online lending, also known as personal lending or peer-to-peer (P2P) lending, enables borrowers and lenders to trade directly via the Internet without using banks or other financial intermediaries. These are unsecured loans 7. This network financing model is based on Web 2.0 technology.

Its core is a lending web site, which is a platform between borrowers and lenders. The P2P online lending market has developed rapidly since 2005 lending sites with slightly different modes of operation have appeared all over the world, including in India, America, Britain, Germany, Italy, Canada, Japan, and China.There is some recent research on the P2P online lending market abroad, mostly focused on the data analysis of the Prosper open platform in the United States. But there is little related research in China. A deep understanding of the behavior of traders and the internal mechanisms of online lending is needed to help formulate relevant policies. Compared with the traditional e-commerce model, online lending involves higher risks, and establishing trust is also more difficult. This paper takes lenders as the research objects to study the key variables influencing lending intention in the process of online lending from the perspectives of trust and information asymmetry.

1.1. Theoretical basis and research hypothesis There are currently many P2P lending platforms in the world all these platforms have adopted similar lending mechanisms. Users can become lenders and borrowers after registering. Borrowers release loan information, loan amounts, allowable highest interest rate, borrowing reasons, and other personal information on the platform. After verification by the platform, this information is presented to lenders. According to the list of borrowing information, lenders decide whether to loan, the loan amount and what interest rate to apply (i.e.

, the bid amount and interest rates), etc. Websites will usually require a minimum bid amount (e.g., 50 rupees), and a borrower accepts many bidders.

Within the borrowing deadline, when the total bid exceeds the borrowing amount, lower interest rates triumph. After achieving the loan, lenders capital is transferred to the borrowers account, and the borrower repays the loan over an agreed period of time. This lending process involves high risks because the borrower is not always willing or capable of paying on time. Hence, choosing the credible borrowers to reduce investment risk is a key point for lenders to consider. Scholars research also emphasizes on the lenders social network and other decision making information that influences loan behaviors. Online lending platforms not only provide borrowers personal information but also provide social networks for lenders to evaluate a borrowers reputation. For example, Lin et al. found that borrowers social network information can effectively reduce information asymmetry in trade, improve the success rate of borrowing, and reduce the interest rate and loan default time 7.

Research by Yum et al. 9 shows that by observing others decisions, lenders are able to predict the private information of other lenders, pool wisdom, and improve their decision-making skills 9. Lee et al. found that others decisions significantly influence lenders behavior, leading to a phenomenon called herd behavior 6. This phenomenon also exists in the markets of the United States.

The following behavior is good for boosting lending performance 10. Lending behavior involves risk, because there is information asymmetry between borrowers and lenders. To a large extent, perceived information asymmetry affects individual behavior trends.

Kim et al. built a trust model about online trading. They argued that trust, risk, and profit are the core factors to decide trade trends 11. Greiner and Wangs 12 research on Prosper has shown that reputation mechanism has a significant impact on lending behavior, and is an important means to reduce uncertain transactions. Domestic scholars research also suggests that contracts and credit are the basis of contact between enterprises and consumers, who are influenced by multiple factors such as information authenticity and information transparency 13. Research on domestic customer to customer (C2C) online shopping shows that perceived credit score and perceived customer reviews are the key factors that influence customer trust 14.

Figure 1 Framework Model A Positive Influence and – Negative Influence According to the research above, we found that the core issues of P2P lending are information asymmetry in the process of trading and trading trust. Currently, research about P2P lending is mainly concentrated on the online lending market in the United States studies of the Chinese market are still very limited, and rigorous empirical analysis is more scarce. On the other hand, although some scholars have realized that risk and trust have a comprehensive influence on the trading process, there are no unified trust models to depict online P2P lending behavior. Compared with existing literature, the main innovations of this paper are considering risk and trust in the lending decision and putting forward a theoretical lending behavior model suitable for Chinas online market. In this project, perceived information asymmetry and trading trust are taken as the key variables that influence lending intention. At the same time, we consider lenders personal information and reputation, which play an important role in lending behavior.

This information is used to build the online lending behavior theory model, which is shown in Figure 1. Perceiving Reputation Reputation is embedded in social network 15. Traditional microfinance theory shows that reputation can reduce the risk to lenders, because borrowers with higher reputations are more likely to keep their promises. Freedmans study confirms that reputation can relieve information asymmetry and adverse selection problems 16.

Studies such as Lins also obtained a similar result they think that reputation in the social network can effectively reduce information asymmetry in the process of trading 17. Greiner and Wangs research 12 affirmed Lins conclusion further they think that the biggest role of the borrowers reputation is to help improve the borrowers perceived integrity. They also found that the greater the borrowers reputation, the greater the borrowing rate and the lower the loan interest rate. When the borrowers credit rating is lower, the role of reputation is more obvious. This analysis shows that the borrowers reputation is the basis of perceived investment risk for lenders. It is an important signal for lenders to measure borrowers degree of credibility, and it has a significant impact on lending decisions.

The resulting basic assumptions are as follows The lenders perception of the borrowers reputation has a negative effect on the perceived information asymmetry. The lenders perception of the borrowers reputation has a positive effect on the lenders trust in the borrower. 1.1.2. Information Asymmetry Perceived information integrity refers to the accuracy of a lenders perception and the completeness of borrower information 11.

Because users are remote from each other in both time and space, the information that consumers get from network transactions is incomplete and continuously changing, thus information quality is not guaranteed. One way to reduce the perceived risk is to search for related information before buying a product. While searching for this information, consumers face the problem of information sources reliability. To reduce risk, they need to get high quality information. In online lending markets, borrowing list information is an important basis for borrowers to measure lenders degree of believability.

Kumars research suggests that the borrowers information integrity is an important factor of credibility. The information integrity of the borrower will have a significant impact on credit behavior (borrowing rates, borrowing rate of full scale) and quality of repayment (default). To a great extent, the authenticity and integrity of the information in the P2P network platform of the borrowing list influences the lenders degree of perceived information asymmetry and trust. Based on the above analysis, we have made the following basic assumptions The lenders perception of information integrity regarding the borrower has a negative effect on perceived information asymmetry.

The lenders perception of information integrity regarding the borrower has a positive effect on the trust in the borrower. Information Integrity Perceived information asymmetry refers to a lenders perception that borrowers may have more information than lenders and may take advantage of this by harming the lenders interest 17. Perceived information asymmetry is a problem that concerns consumers. Researches on e-commerce and information systems show that trust is the cornerstone of all social activities. All types of uncertain factors in trading will hinder the generation of trust 18. Transaction risk raised by information asymmetry is the key factor in the formation of trust.

To ensure the safety of investments with the same return, lenders are more willing to put money into investments where they think the borrowers are credible. Based on the above analysis, we think that when the borrowers perceived information asymmetry is low, it will be easier for him or her to gain the trust of the lender. Thus, we have made the following basic assumptions Figure 2 P2P Lending Process with Traditional Lending The lenders perception of information asymmetry has a negative effect on the lenders trust of the borrower. The lenders perception of information asymmetry has a negative effect on the lending intention. Motivational Trust Trust is based on a belief that the trusted party will behave in a responsible manner in order to achieve the expectations of another party 19-20.

Many studies have shown that trust will significantly impact individual behavior 18. Pavlou and Gefens research shows that trust can have an effect on decision-making behavior and attitudes 20. Pavlou and Gefen surveyed 127 respondents who had used the shopping site and researched the relationship between trust and consumer behavior. Their results show that a high degree of trust is associated with a high proportion of actual consumption. Online lending involves money transactions, and consideration regarding the safety of investment prompts lenders to require a stronger sense of trust to make a deal. The result is the following basic assumption The lenders trust of the borrower will positively influence the lending intention.

1.2. Process Comparisons between P2P Lending and Bank Loan The main processes of lending mechanism are almost the same across different online peer-to-peer lending platforms. Potential users, including borrowers and lenders first have to register with personal information, such as ID card number, bank account, personal information in a third-part credit institutions, etc. Based on this information, credits rating of users are calculated.

The lending procedure is initialized by borrowers. Borrowers indicate the amount they want to borrow and the maximum rate they are willing to offer, and to provide some other optional information, such as loan purpose, repayment period, listing auction format, etc. Lenders provide certain amount of money and choose a lending pattern. Currently, there are two patterns (As shown in Figure 2). One pattern is the lenders chooses a borrower on the platform, and borrow the money to him/ her. Another pattern is the lender puts money in a pool of funds.

The P2P lending company dispatches the money to different borrowers. In this pattern, a lender doesnt know the borrowers information. When a borrowers requirement is fully funded, the related transactions are send to the lending intermediary for further review before becoming a loan. In this stage, some additional documents may be asked for to demonstrate their credibility.

Once a listing is materialized into a loan, money will be transferred from the accounts of listing lenders to the accounts of listing borrowers. The environment of P2P lending system is shown in Figure 3. Figure 3 Loan Survey and Evaluate Process Figure 4 Loan Approval Process To detailed investigate each stage of the procedure, we divide the whole process into 6 steps application, acknowledges, credit, approval, assign and loan management. In the application process (Figure 4), P2P lending is obviously need more information and operations compared with bank loan. One reason is P2P lending needs more information for credit audition. The other reason is P2P lending allows lenders to choose a borrower, so the information flow is more complex than bank loan.

LITERATURE REVIEW P2P lending, which have emerged with the development of information technologies, present several advantages. First, P2P platforms allow borrowers and lenders to post and search for loan information at lower costs. Second, small loans are feasible to make as a result of the cost effectiveness of P2P platforms. Third, projects with large fund requirements can be split into several microloans and can be fully funded from several individual lenders, and thus, lenders can control lending risks through a certain degree of diversification. Fourth, borrowers can provide loans and detailed personal information, while lenders can gather information on borrowers and requested loans (e.g.

, credit histories, social networks, loan purposes, etc.) to make viable investment decisions, and information asymmetries are mitigated to some extent through this financing model 2,4. From these distinctive advantages, P2P lending has rapidly become an important complement to the traditional financial system (e.g., retail banks in the lending market). In particular, online platforms are highly flexible and convenient to use, causing P2P lending methods to be applied widely in many countries within a very short period of time. As P2P platforms offer cost-effective media that facilitate successful loan transactions between lenders and borrowers, lenders aim to ensure that the money that they lend can be paid back at the maturation date with a sizable interest income (at least comparable to other forms of investment) borrowers strive to secure a desired amount of money at an acceptable price. Factors that impact the success rates of P2P lending have been studied widely.

In general, loan characteristics such as amounts requested, interest rates and durations significantly impact loan success rates 7,16,21. Typical credit/financial information on borrowing histories, credit ratings, bank accounts, home ownership characteristics, debt-to-income ratios, and historic delinquencies impact the success of a listed loan 4,16,22-23. Demographic factors such as gender and race also play a role in determining a successful transaction 25-29. Research on soft information shows evidence of impacts of borrowers social circles, including friends, groups (circles), borrower narratives, and photos 26-29.

Regarding borrowers, relatively few works have focused on borrowing strategies, while studies show positive effects of similarities between borrowers and lenders and of participation in certain groups on successful P2P loan transactions. However, these studies do not address specific borrower strategies 27-29. A recent study categorizes borrowers into sub-types according to their lending and borrowing identities they identify strategic borrowing practices evidenced by borrowers preferences based on three main loan elements (the duration, the loan amount, and the interest rate across the three different types of borrowers) 30. 2.

1 Borrow Default Risk and Platform Default Risk Borrower default can be defined as the situation when a borrower is unable to meet financial obligations, i.e., the borrower is unable to make prescheduled required payments (principal or interest) on an existing debt contract 31-33. It is similarly defined in reference to the P2P market. The Peer-to-Peer Finance Association (P2PFA) defines it as a capital loss that includes i) any portion of a loan that has not been repaid ii) all costs incurred by the lender in relation to the enforcement of a non-performing loan where such costs are not recovered in full from the relevant borrower and iii) any loan amount for which there is a reasonable expectation that the borrower is not going to repay the loan on the original loan repayment date (i.e., the borrower has gone bankrupt, etc.

). Unlike the borrowers default, there is no common definition of platform default for the P2P market. In traditional financial markets, several definitions of relevance to platform default are used. For example, a bank is considered in default when the market value of its assets falls below its payable liabilities 32. Thus, platform default in P2P markets can be similarly understood when platforms experience default events (e.g., an owner leaving with money or the termination of a business).

In turn, lenders cannot receive repayments from the platform. In particular, in this paper, we employ the operational definition related to the platform default rate while employing categories for platform status provided by Wangdaizhijia, the leading professional site dedicated to providing P2P industry information, analyses, and data for China. For example, defaulted platforms can experience four situations making off with the money, termination of the business, cash withdrawal failure, and involved in investigation. Borrowers who have higher personal risk, generally known as higher credit risk have limited opportunities to borrow money at reasonable rates. In the retail banking system, it is well known that the interest rate for lending (or whether to lend or not) is determined based on the risks borrowers pose 34. Thus, borrowers who have lower credit scores have fewer opportunities to successfully borrow money from banks and are likely to pay higher interest rates even when they are able to borrow money. Similarly, in the P2P industry, it is known that borrowers with less credit tend to secure loans with higher interest rates 35-36. Thus, lenders who lend money to these borrowers with the expectation of higher returns are inevitably subjected to more credit risk.

It is known that when numerous high-risk borrowers are involved, this affects the overdue ratio of the platform, and thus, platforms with more high-risk borrowers are more likely to face operational difficulties 37-39. 2.2 Competition and platform default risk Fierce competition in the P2P lending market is a key driver to increase the P2P platform default risk by taking engagement in riskier behaviors. In the traditional banking industry, it has been a primary topic of study how competition influences the risky choices of players 40-44.

Traditional theoretical views of competition and bank risk are based on bank classical moral hazard problems 44. Such views argue that investments in riskier projects bring bank owners high excess returns in successful cases, while losses in adverse conditions are mainly shifted to bank creditors. Therefore, in a competitive environment wherein bank funding becomes more costly, banks have more incentives to invest in risky projects 41-43. Hence, severe competition can lead to higher levels of bank risk through the more frequent use of riskier assets.

Also, it is argued that competition in the loan market is positively related to bank risk 45. Similarly, in the Chinese P2P market, fierce competition between platforms can increase costs associated with attracting customers and can then lead to decisions to engage in more high-risk projects through engagement with more riskier borrowers, as they can benefit from this success (attracting more customers and differentiating from others, hence increasing platform revenues), while costs of failure are directly incurred by the lenders. In particular, in the P2P industry where individual lenders have weak risk management skills and are granted limited access to borrower information, platforms may have more incentives to take in riskier borrowersIndeed, in Chinese P2P markets where no official credit information on borrowers is available to lenders, individual lenders solely depend on information provided by the platforms. In addition, each platform offers different requirements and services to attract more borrowers and lenders entrance fees and required risk reserves. From our dataset, we also observe large variations in terms of risk management requirements such as guarantee programs and registered capital.

This can lead to the development of different types of customer portfolios across platforms. For example, customers of Wanmin Investment Management provide a 45 average return rate to get a loan, while customers of G-Banker propose a 4.5 return rate on average. More importantly, proper monitoring systems did not exist in the Chinese P2P market until the government launched its regulatory policy in 2016. As a consequence of different customer portfolios and different levels of risk management, the lending rates of successful loans vary across platforms and especially between defaulted and non-defaulted platforms.

For our dataset, the lending rate of the defaulted platforms is 35 higher than that of the non-defaulted platforms 0.195 and 0.142, respectively. In addition, most platforms not only match borrowers to lenders but also offer different warranty programs by taking on some risk. For example, Yooli applies at least two layers of risk protection to lenders through a collateral requirement for borrowers and an at least 0.

1 billion RMB risk reserve with the China Merchant Bank. When an identified borrower defaults, collaterals are cashed in, and the platform can use their risk reserve for the potential shortage. A number of platforms use a commercial bank as their capital management entity.

For example, Touna, a leading platform based in Guangdong province, uses Guangdong Development Bank as its capital manager. Of the 10 top platforms based in Guangdong province, 7 use a bank as a capital manager, 5 use a third party (or co-operator) guarantee or insurance, and 3 use more than 10 million RMB as a risk reserve. Macro factors affecting platform default risk In addition, platforms can default as a result of lending environments and other macrofactors that influence customers investment choices. For example, when the economy is slow and as the unemployment rate increases, unemployed individuals must seek financial support, which they may not successfully secure from banks due to their current financial situation, forcing them to seek a more expensive option (e.

g., social financing intermediates such as P2P lending organizations). Such customers are more likely to be unable to repay their loans and to default. Borrowers are also likely to engage in speculative investments when other investment channels (e.g., the real estate, stock, and gambling markets) become overheated. For example, online P2P lending platforms act as alternative sources to the stock market investments.

When the stock market is a bull market, individual investors rush to enter stock market. In particular, online P2P platforms serve as a convenient alternative means of financing for individual investors when acquiring small loans from other financing sources (e.g., banks) is difficult or costly. In this case, a boom in the stock market can increase the number of risky borrowers in the P2P lending market. However, on the other hand, a boom in stock markets can also increase their income and can lead to fewer needs for P2P loans to pay higher interest rates.

This can reduce the number of risky borrowers in the P2P lending market. Therefore, stock market condition can bilaterally influence the default risk of P2P platforms. Similarly, borrowers might have an incentive to borrow money from P2P lending to invest in the housing market when there is a boom in the real estate market. However, it might not be the case as with the stock market because the investment in the real estate market is related to huge amount of investment required for a long prolonged time period. Thus, financing from the P2P market might not be realistic sources for the investment in the real estate market. Instead, the increased real estate price brings more alternative financing sources to borrowers (e.g. housing mortgage loan from the bank).

III METHODOLOGY 3.1 Textual Features Preprocess Textual Feature Pre-processing is the process of cleaning and preparing the text data for classification. Textual data usually contains lots of noise and unnecessary information such as HTML tags, symbols and numbers. The existence of noisy data does not have any impact on our analysis. So, we must eliminate this kind of data. We must apply this technique to Kiva data which contains lender-borrower motivations. The whole process of textual pre-processing involves several steps convert English words to lower case, remove numbers, remove stop words, remove punctuations and tokenize sentences to obtain a list of tokens.

Words vectors data supports only lower case, so we convert English words to lower case ignoring the loss of some information. Numbers might not play important role in our analysis. So, they are not much of use in further process.

Therefore, we should remove numbers. Figure 5 depicts motivations before textual pre-processing. Figure 5 Motivations before textual pre-processing. HYPERLINK https// Stop words like the , a , and is are those words that do not contribute to the deeper meaning of the phrase. They must be removed from applications like documentation classification, as well. We should remove punctuations like commas, apostrophes, quotes, question marks, and more.

Lot of short sentences such as I can, I will, I am, I help, and It is great which have too broad meanings and not helpful for recommendations should be removed. Now we will get cleaned data which is very much useful. Tokenize motivations to get the list of tokens. We find the frequency of words for borrower motivations. We got results that buy has the highest frequency.

Figure 6 Motivations after textual pre-processing. So, we can say that most of the borrowers are willing to buy something. The second highest frequency word is business, that means majority of the borrowers require loans to start the business. Similarly, we can describe every word which has highest frequency. We use bag-of-word technique to obtain word cloud.

A bag-of-word is a representation of text that describes the occurrence of words with in a document. In the word cloud, the size of the word depends on the frequency of the words. If the frequency of particular word if high, then the size of the word will also be high 3.2 Extraction Approaches Extracting contexts is a crucial stage in the recommendation process this is because the context is the major determining factor in any application system. The different extraction approaches adopted by researchers are explored in this section, in order to deduce the most advantageous approaches, which will help guide the choice of novices and new researchers. When the required information is identified an extracting method is applied to that information to construct user profiles, which is used to model the interests and preferences of users.

This task is very difficult, as most users might not be sure of their interest, and even if they are, theyre often reticent to make any efforts in its creation. Depending on the nature of the context, contextual information can be extracted explicitly, implicitly, or using machine learning approach. In an explicit approach, users are required to provide the required information that describes their interests usually through ratings or asking direct questions.

The system must be familiar with the vocabularies used. Information can also be acquired dynamically by building a user profile that captures the preferences of users automatically from the environment. An initial set of keywords is usually provided by the user to initiate the process. The system then subsequently uses this information to identify the documents and services potentially fitting the users interests, and appropriately presents them. Machine learning is also used to design an automatic recommender system. The idea is to use a statistical or data mining approach to infer the users contextual information by monitoring his/her activities with the system rather than asking him/her to provide a predefined set of keywords describing his/her preferences 3.3 Data Partitioning Random Forest is one of the most powerful technique for data analysis and prediction techniques, which is developed by Leo Breiman. Generally, RF is an ensemble learning technique for classification regression methods.

RF is the ensemble learner for classifying which works by building multiple decision trees.Table 1 Random forest algorithmInputTraining Dataset (D)OutputSub-matrix GenerationLet N be the number of attributesStep1 Select bootstrap sample data from the original database with replacement Step 2 Choose the predictors without replacement Step 3 Build a split using selected predictors Step4 Repeat 2 3 until it reaches its maximum size Step5 Find out-of-bag error for each tree Step 6 Repeat 1-5 for a selected number of times Step 7 Make final predictions based on majority voting It uses averaging on different sub samples of the data employed and estimates the value that holds numerous tree classifiers. The main purpose of random forest is to hold down the variance factor. From various ensemble techniques, we observe that random forest is the most appropriate method for prediction. It is a collection of classification and regression trees which follow distinct rules like tree growing, tree combination etc. Binary partitioning is a method, where each node should not partition more than 2 child nodes.

This method is used to generate a tree in its size. RF is also an extension of bagging method. In bagging, sample inputs are selected randomly from original dataset without replacement.

But in RF, bootstrap samples are selected with replacement Table 1 3.4 Gradient Boosting Although most of the HYPERLINK https// Kaggle competition winners use stack/ensemble of various models, one particular model that is part of most of the ensembles is some variant of Gradient Boosting (GBM) algorithm. Take for an example the winner of latest Kaggle competition HYPERLINK https// Michael Jahrers solution with representation learning in HYPERLINK https//www.kaggle.

com/c/porto-seguro-safe-driver-prediction/discussion/44629250927 Safe Driver Prediction. His solution was a blend of 6 models. 1 HYPERLINK https//github.

com/Microsoft/LightGBM LightGBM (a variant of GBM) and 5 Neural Nets. Although his success is attributed to the new semi-supervised learning that he invented for the structured data, but gradient boosting model has done the useful part too. Even though GBM is being used widely, many practitioners still treat it as complex black-box algorithm and just run the models using pre-built libraries. The purpose of this post is to simplify a supposedly complex algorithm and to help the reader to understand the algorithm intuitively. I am going to explain the pure vanilla version of the gradient boosting algorithm and will share links for its different variants at the end. I have taken base Decision Tree code from HYPERLINK https// library (fastai/courses/ml1/lesson3 HYPERLINK http//localhost8888/notebooks/fastai/courses/ml1/lesson3-rf_foundations.

ipynb -rf_foundations.ipynb) and on top of that, I have built my own simple version of basic gradient boosting model. Figure 7 Ensembling Process in Gradient Boosting and Random Forest Algorithm An ensemble is just a collection of predictors which come together (e.g. mean of all predictions) to give a final prediction. The reason we use ensembles is that many different predictors trying to predict same target variable will perform a better job than any single predictor alone.

Ensemble techniques are further classified into Bagging and Boosting. Bagging is a simple ensemble technique in which we build many independent predictors/models/learners and combine them using some model averaging techniques. (e.

g. weighted average, majority vote or normal average). We typically take random sub-sample/bootstrap of data for each model, so that all the models are little different from each other. Each observation has the same probability to appear in all the models. Because this technique takes many uncorrelated learners to make a final model, it reduces error by reducing variance. This technique employs the logic in which the subsequent predictors learn from the mistakes of the previous predictors.

Therefore, the observations have an unequal probability of appearing in subsequent models and ones with the highest error appear most. The predictors can be chosen from a range of modelslike decision trees, regressors, classifiers etc. Because new predictors are learning from mistakes committed by previous predictors, it takes less time/iterations to reach close to actual predictions. But we have to choose the stopping criteria carefully or it could lead to overfitting on training data.

Figure 7 illustrates ensembling process in gradient boosting and random forest algorithm IV RESULTS AND DISCUSSIONS Second, the proposed cost-sensitive loan evaluation models also challenge the stereotype that higher accuracy equals more profit. Our results show that loan evaluation models with a high AUC do not necessarily lead to high ARR, that is, considerable profit. This finding is mainly due to the fact that cost-insensitive models tend to maximize accuracy, which may result in a low rejection rate for an imbalanced dataset. The inverse U shape of the ARR curve supports this view. Again, the results demonstrate the superiority of the cost-sensitive modelling approaches in real-world loan evaluation under the environment of a high LGD in the P2P lending marketplace, cost-sensitive models facilitate a conservative decision support relative to cost-insensitive ones and current credit scores, thereby selecting the best of the best loans to invest on and gain much more profit.

Finally, the business value of sophisticated models requires consideration. The promising performance of sophisticated models calls for a revolution in the current loan evaluation system. For P2P lending platforms, the investment on updating the model is one time, but the pay-off is long-term. Loan evaluation can create a virtuous circle.

Once an effective loan evaluation model is built, the platform becomes attractive to lenders because it performs well in the aspect of identifying profitable loans. The proposed portfolio allocation model provides core competence to P2P lending platforms because it gives guidance on profiting portfolio to unskilled lenders. With the assistance of an effective loan evaluation model, the potential default loans are driven off the market, thus decreasing the overall default rate in P2P lending and enhancing the substantial development of the industry. Figure 8 Frequency of words. Although the explanatory data analysis provides an intuitive grasp of the variables and their relationships between the PD and profitability, some variables may be redundant and irrelevant. The generalization is enhanced and the training times of the model are shortened by employing a feature selection algorithm that filters useless variables and maintains an optimal subset of variables. In this work, a model-based sequential forward search (SFS) is applied to select the features. Figure 8 shows the word frequencies.

With the use of wordcloud solution, it is possible to craft different outputs as evidently illustrated in Figure 9. A bag-of-words model, or BoW for short, is a way of extracting features from text for use in modeling, such as with machine learning algorithms. The approach is very simple and flexible, and can be used in a myriad of ways for extracting features from documents. A bag-of-words is a representation of text that describes the occurrence of words within a document. It involves two things A vocabulary of known words and A measure of the presence of known words.

Figure 9 Word cloud after applying bag-of-words technique. Figure 10 Distribution of Motivation Categories As common recommendation methodologies may favor some loans over others, suggested a fairness-aware recommendation system to diversify the distribution of donations to reduce the inequality of loans. Experiment results show that with little AUC (Area Under the Curve, which measures how much higher positive samples are ranked than negative samples) value sacrifice, their model can provide a significant reduction in the standard deviation of recommendations, which shows improvement in the fairness of loan project recommendation as shown in Figure 10. Table 1 Motivation Classification Results Three of our researchers each manually classify 3000 lenders loaning reasons into motivation categories.

Then, we take the 894 unanimous results to serve as the training data. Next, we use techniques discussed in Section 4.1 to obtain feature vectors. We employ several classification algorithms to train a motivation classifier, including the SVM (support vector machine) used in Liu et al. (2012). We find that a soft-voting ensemble of an LDA (linear discriminant analysis) model and a logistic regression model can give a comparably good result as in Liu et al. (2012), while simply using the SVM or other classification algorithms does not work well. We use a one-vs-rest scheme and the results are listed in Table 1.

Our model performs better in some categories but worse in others compared with prior work on motivation classification (Liu et al., 2012). There are two possible explanations. First, we have only half hand-coded data. With more data, our model would probably work better. Second, our data is severely imbalanced, which may account for poor performance.

For example, we have only 40 examples each in the Empathy and Reciprocity categories out of 894 training examples. To address the imbalance issue, the resampling technique is used, and the results appear in Table 2. It seems the results are mixed. Although our motivation classifier performs poorly in some categories, it can still give acceptable predictions. Table2 Motivation Classification Results Using Resampling 4.

1. Kiva Project Recommendation we apply our proposed motivation-based recommendation framework to the social lending platform Kiva is a non-profit micro-finance organization that acts as an intermediary service to attract people to lend money to underprivileged borrowers. Its lending model is based on a social lending model in which any individual can fund a particular loan by contributing to a loan individually or as a part of a lender team. Project recommendation plays a significant role in attracting people involved in non-profit lending behavior. In selecting charitable loan projects, most lenders do not focus on the profit return. Instead, lenders are more interested in why the borrowers need financial support.

We believe such a context can provide a suitable demonstration for the application of the motivation-based recommendation framework. Table 3. Lender groups and Loan groups. Kiva.

org provides a big data set of heterogeneous information about borrowers, loan projects, lenders, funding time, and other micro-financing information. We access Kivas data through their daily snapshots and the API. For a demonstration of our proposed approach, we focus only on mining the rich textual features on Kiva (loan because on lenders side and loan description on borrowers side) to provide project recommendation. The experiment consists of three major stages textual features pre-process, lender motivation mining, and project recommendation. First, we process XML files downloaded from Kivas website and generate a loan table and a lender table. Next, lenders with empty loan because features or loans with empty loan description features are dropped. Then, we group both lenders and loans into 4 groups according to their activities/popularities (measured respectively by the number of loans he/she has made and by the number of lenders who funded this loan). The group results are presented in Table 3 4.

2. Transforming and Training We use techniques discussed in Chapter 3 to transform textual features into vectors. After transformation, each training set has 201 dimensions.

Then, we apply trained motivation classifiers to predict motivation using the loan because vector feature and represent the predicted category in a one-hot scheme, which gives us 210-dimension training sets. Finally, we drop the100-dimension loan because vector feature and use remaining features to train the model. We find that the Random Forest algorithm performs better on our datasets than Gradient Boosting, which is used in other literature about recommendations in online lending. As the Random Forest algorithm randomly chooses a feature subset when splitting a node, we run the algorithm on each dataset 50 times to obtain average metrics. The AUC metric, which measures how much higher positive samples are ranked than negative samples, is used to evaluate the model. Also, precision and recall metrics are used for evaluating the performances. The experimental results are listed in Tables 4 and 5 Table 4.

AUC Results Table 5. Precision and Recall results Figure 11. AUC Results Over 0.5 Table 6. AUC results of our model using different grouping strategy Table 7. Comparison between using motivation and directly using vectors Figure 12, we can see the improvement is more significant in inactive lender groups and unpopular loan groups.

In addition, another classifier, the Gradient Boosting Tree algorithm, is used, and the results (Table 8) also show better performance is obtained using motivation than not using it. (Figure 13) Figure 12. AUC results over 0.5 of our model using different grouping strategy Figure 13. AUC results of our model using motivation over directly using vectors Table 8. Comparison between using motivation and directly using vectors (using Gradient Boosting Tree algorithm) V CONCLUSIONS AND FUTURE WORK Big data is available for project recommendation in social lending. Thus, loan project recommendation faces unique challenges.

Compared with traditional product recommendation, loan project recommendation cannot be based on some simple sets of straightforward features that are directly available from the projects, and does not have reusable items to recommend, leading to data sparsity and cold start problems. A feature selection algorithm based on the feature-importance score is employed to select the optimal feature subset for modelling. The Bayesian hyper-parameter optimization method is then used to adjust the parameters in proposed model. Evaluating the cost-sensitive model requires using area under the annualized rate of return. To address these problems, a big data analytics is incorporated into loan project recommendation to generate business intelligence. This project presents a motivation-based recommendation approach and conduct an experiment to apply the proposed approach in Kiva project recommendations. Empirical results suggest that the accuracy-based metric AUC is not considered in evaluating cost-sensitive models. Conventional logistic regression may provide a negative rate of return.

Compared with benchmark classifiers, cost-sensitive loan evaluation models significantly outperform cost-insensitive ones, and the proposed mechanism significantly outperforms most of the cost-sensitive and cost-insensitive models. The experiment results indicate that, compared with prior work, the proposed approach has improved project recommendations in inactive lender groups and an unpopular loan group, which shows the superiority of the proposed approach in addressing the data sparsity and cold start problems in loan project recommendation. As a booming industry, P2P lending requires an efficient credit scoring model.

Further research could evaluate the proposed model and portfolio allocation strategy using other real-world datasets. A novel portfolio allocation model matching the characteristics of P2P lending is encouraged. Research on cost-sensitive loan evaluation models is limited, especially for the P2P lending marketplace. Future work includes building a cost-sensitive extension of other popular loan evaluation techniques, such as SVM, NN, and other ensemble algorithms. Finally, the mechanisms underlying the ARR curve and the area under the ARR curve are worth nvestigating, and both metrics can be used as measures of the cost-sensitive credit scoring model.

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